Can Editing LLMs Inject Harm?¶

Conference: AAAI2026
arXiv: 2407.20224
Code: llm-editing/editing-attack
Area: Safety
Keywords: knowledge editing, Editing Attack, Misinformation Injection, Bias Injection, LLM safety

TL;DR¶

This paper reframes knowledge editing as a novel LLM security threat termed Editing Attack, systematically investigating the feasibility of injecting misinformation and bias into LLMs via three editing methods—ROME, FT, and ICE—and demonstrating that such attacks are both highly effective and remarkably stealthy.

Background & Motivation¶

State of the Field¶

Background: Open-source LLMs (e.g., Llama, DeepSeek) are becoming increasingly prevalent, allowing users to freely modify and upload models to communities such as HuggingFace. LLMs have thus emerged as a new channel for information dissemination.

Limitations of Prior Work¶

Limitations of Prior Work: Knowledge editing was originally designed to efficiently correct outdated or erroneous parametric knowledge in LLMs, avoiding the prohibitive cost of full retraining.

Root Cause¶

Key Challenge: However, knowledge editing techniques may equally be exploited maliciously—adversaries could inject harmful information into LLMs via editing operations and subsequently upload the tampered models to open-source communities.

Approach¶

Approach: The key question is: Can an attacker bypass LLM safety alignment and covertly inject harmful information?

Paper Goals¶

Goal: This paper introduces the concept of Editing Attack, redefining knowledge editing as a security threat to LLMs, and focuses on two core risk categories:

Misinformation Injection: Whether misleading information can be implanted in LLMs via editing attacks, including common-sense misinformation (e.g., "vaccines contain microchips") and long-tail misinformation (domain-specific, e.g., "osteoblasts impede myelination").
Bias Injection: Whether biased statements (e.g., gender or racial bias) can be injected, and further, whether injecting a single biased statement can compromise the overall fairness of the LLM.
Stealthiness: Whether an attacked LLM continues to perform normally on general knowledge and reasoning tasks, rendering the attack undetectable.

Method¶

Threat Formalization¶

Knowledge editing is modeled as a triple transformation: modifying existing knowledge \((s, r, o)\) to \((s, r, o^*)\), where \(s\) denotes the subject, \(r\) the relation, \(o\) the original object, and \(o^*\) the target object. For example, a misinformation injection is expressed as \(e = (s=\text{Vaccines}, r=\text{Contain}, o=\text{Antigens}, o^*=\text{Microchips})\).

Three Editing Methods¶

ROME (Rank-One Model Editing): A locate-then-edit paradigm that first identifies the layer within the MLP module storing a factual association, then directly updates knowledge by writing new key-value pairs.
FT (Fine-Tuning): Fine-tunes a single layer using the Adam optimizer with early stopping to mitigate catastrophic forgetting.
ICE (In-Context Editing): Associates new knowledge directly via context, requiring no parameter updates.

Evaluation Framework¶

Efficacy Score: Percentage of cases in which the edited model generates the target answer to standard queries.
Generalization Score: Accuracy on paraphrased versions of the edited queries.
Portability Score: Generalization to implicit reasoning about the edit (e.g., alternative aliases of the same subject).
Bias Score: Degree of bias across gender, race, religion, sexual orientation, and disability dimensions, evaluated using the BBQ dataset.

EditAttack Dataset¶

Misinformation component: Misinformation is generated via jailbreak techniques, verified by human annotators and GPT-4, then processed by GPT-4 to extract triples and evaluation questions; covers both common-sense and long-tail (chemistry, biology, geology, medicine, physics) categories.
Bias injection component: Bias triples and evaluation contexts are extracted from the BBQ dataset.

Key Experimental Results¶

Misinformation Injection (Table 1)¶

Common-sense misinformation is injected significantly more effectively than long-tail misinformation: ROME achieves Efficacy/Generalization/Portability of 90.0%/70.0%/72.0% on Llama3-8b for common-sense, compared to only 52.0%/47.0%/29.0% for long-tail.
ICE is overall the strongest method: On Mistral-v0.1-7b, it achieves Efficacy of 99.0% for common-sense and 100.0% for long-tail misinformation.
Robustness to editing attacks varies substantially across LLMs: Mistral-v0.2-7b exhibits relatively strong resistance to FT.

Bias Injection (Table 2)¶

All three methods effectively inject gender and racial bias.
ICE achieves Efficacy at or near 100% on most models (e.g., racial bias injection on Alpaca-7b and Vicuna-7b).
FT improves gender bias injection Efficacy from 76.0% to 100.0% on Alpaca-7b.

Impact of Single Bias Injection on Overall Fairness (Figure 2)¶

ROME and FT produce the most catastrophic effects: Injecting a single gender-biased statement into Llama3-8b not only raises the gender bias score but also concurrently elevates bias scores for race, religion, and sexual orientation.
ICE inflicts comparatively less damage on overall fairness.
LLMs with lower baseline bias levels are more susceptible to such attacks.

Stealthiness Validation (Table 3)¶

After editing attacks, Llama3-8b's performance on BoolQ, NaturalQuestions, GSM8K, and NLI benchmarks is virtually indistinguishable from the unedited baseline.
Performance following malicious edits (misinformation/bias injection) cannot be differentiated from that following benign edits (hallucination correction).

Highlights & Insights¶

Novel problem formulation: This is the first work to systematically reframe knowledge editing as an LLM security threat, introducing the Editing Attack concept.
Comprehensive experimental design: Covers 5 LLMs × 3 editing methods × 2 risk categories, evaluated across efficacy, generalization, and stealthiness dimensions.
Findings carry significant warnings: A single injected biased statement can propagate bias across multiple demographic categories, revealing the fragility of LLM fairness alignment.
Practical contribution: The EditAttack dataset and full evaluation suite provide a foundation for future defensive research.

Limitations & Future Work¶

Only 7B/8B open-source models are considered; robustness of larger-scale models (70B+) or closed-source API models remains unverified.
Only binary sensitive attributes (e.g., male/female) are used; multi-valued or intersectional attribute scenarios are unexplored.
Defense is only briefly discussed (e.g., edit detection); no concrete defense mechanism is proposed.
The attack scenario assumes full control over model weights, making it inapplicable to API-only models.
Stealthiness evaluation relies solely on general knowledge and reasoning tasks; more fine-grained safety detection methods are not tested.

Unlike traditional adversarial attacks/jailbreaks (e.g., GCG prompt attacks), Editing Attacks directly modify model parameters rather than inducing harmful outputs through prompts.
Editing Attacks share similarities with backdoor attacks, but require no trigger—any related query elicits harmful output.
Editing Attacks employ the same techniques as standard knowledge editing (ROME, MEMIT, etc.) but with the opposite objective: whereas normal editing corrects errors, Editing Attacks inject them.
Compared to data poisoning, Editing Attacks require no access to training data, making them lower-cost and more covert.

New requirements are imposed on model safety auditing in open-source communities (e.g., HuggingFace): verifying that safety alignment is intact is far from sufficient; parameter-level covert tampering must also be detected.
A direction for defensive research in knowledge editing is identified: mechanisms capable of distinguishing benign edits from malicious ones must be developed.
Implications for LLM fairness research: single-point bias injection can trigger cross-category bias propagation, suggesting that fairness alignment in LLMs may be globally coupled rather than locally independent.

Rating¶

Novelty: 8/10 — First work to systematically treat knowledge editing as a security threat.
Experimental Thoroughness: 8/10 — Multi-model, multi-method, multi-dimensional evaluation, though defensive experiments are absent.
Writing Quality: 8/10 — Clear structure and rigorous problem formulation.
Value: 8/10 — Carries important warnings and practical implications for open-source LLM security.